Automated vehicular accident detection

ABSTRACT

A vehicle accident detection method and system is provided. The method includes receiving location coordinates associated with a location of an occurring vehicular accident. Data associated with possible causes of the vehicular accident is received from sensors. Traffic related rules associated with a geographical location are retrieved and analyzed with respect to the data. Parameters associated with at least one vehicle involved in the vehicular accident and a possible cause are determined via execution of programming logic and transmitted to additional systems. The possible cause for the vehicular accident is determined from all possible causes based on matching current and historical accident circumstances. Additionally, weighting factors may be available and adjusted over time for accurate accident detection. A possible cause comprising a greatest weighting factor may be used to identify a most likely cause.

FIELD

The present invention relates generally to a method for detectingvehicular accidents, and in particular to a method and associatedautomated system for retrieving, storing, and analyzing vehicularaccident related information to accurately determine a cause forvehicular accidents.

BACKGROUND

Detection systems typically reduce issues resulting from automobileaccidents by decreasing a response time with respect to emergencyresponders. Errors with respect to detecting or judging a violation,error, failure, or circumstance associated with a cause of an accidentmay unjustly penalize drivers, insurance companies, and any additionalinvolved parties. The aforementioned errors may be associated withcomplex accident scenarios that if left to manual interpretation may betoo complex for determining an accurate cause. For example, amulti-vehicle accident caused by, inter alia, a texting driver, a hotcoffee spill, a deer cutting across a highway, etc.

Accordingly, there exists a need in the art to overcome at least some ofthe deficiencies and limitations described herein above.

SUMMARY

A first aspect of the invention provides a vehicle accident detectionmethod comprising: receiving, by a computer processor of a computingsystem, location coordinates describing a location where a vehicularaccident occurred; receiving, by the computer processor from a pluralityof sensors, data associated with possible causes of the vehicularaccident; retrieving, by the computer processor, traffic related rulesassociated with a geographical location of the location; analyzing, bythe computer processor executing programming logic, the data withrespect to the traffic related rules; determining, by the computerprocessor based on results of the analyzing, parameters associated withmechanical issues of the at least one vehicle involved in the vehicularaccident; determining, by the computer processor based on results of theanalyzing, distraction parameters associated with distraction relatedevents for a driver of the at least one vehicle involved in thevehicular accident; and determining, by the computer processor based onresults of the analyzing, the parameters, and the distractionparameters, a possible cause for the vehicular accident.

A second aspect of the invention provides a computing system comprisinga computer processor coupled to a computer-readable memory unit, thememory unit comprising instructions that when executed by the computerprocessor implements a vehicle accident detection method comprising:receiving, by the computer processor, location coordinates describing alocation where a vehicular accident occurred; receiving, by the computerprocessor from a plurality of sensors, data associated with possiblecauses of the vehicular accident; retrieving, by the computer processor,traffic related rules associated with a geographical location of thelocation; analyzing, by the computer processor executing programminglogic, the data with respect to the traffic related rules; determining,by the computer processor based on results of the analyzing, parametersassociated with mechanical issues of the at least one vehicle involvedin the vehicular accident; determining, by the computer processor basedon results of the analyzing, distraction parameters associated withdistraction related events for a driver of the at least one vehicleinvolved in the vehicular accident; and determining, by the computerprocessor based on results of the analyzing, the parameters, and thedistraction parameters, a possible cause for the vehicular accident.

A third aspect of the invention provides computer program product forvehicle accident detection, the computer program product comprising: oneor more computer-readable, tangible storage devices; programinstructions, stored on at least one of the one or more storage devices,to receive location coordinates describing a location where a vehicularaccident occurred; program instructions, stored on at least one of theone or more storage devices, to receive from a plurality of sensors,data associated with possible causes of the vehicular accident; programinstructions, stored on at least one of the one or more storage devices,to retrieve traffic related rules associated with a geographicallocation of said location; program instructions, stored on at least oneof the one or more storage devices, to analyze the data with respect tothe traffic related rules; program instructions, stored on at least oneof the one or more storage devices, to determine parameters associatedwith mechanical issues of the at least one vehicle involved in thevehicular accident; program instructions, stored on at least one of theone or more storage devices, to determine distraction parametersassociated with distraction related events for a driver of the at leastone vehicle involved in the vehicular accident; and programinstructions, stored on at least one of the one or more storage devices,to determine based on results of the analyses, the parameters, and thedistraction parameters, a possible cause for the vehicular accident.

The present invention advantageously provides a simple method andassociated system capable of reducing issues resulting from automobileaccidents.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a vehicular accident detection system, in accordancewith embodiments of the present invention

FIG. 2 illustrates an algorithm detailing a process flow executed by thesystem of FIG. 1 during a vehicular accident, in accordance withembodiments of the present invention.

FIG. 3 illustrates a computer apparatus for retrieving, storing, andanalyzing vehicular accident related information to determine a causefor vehicular accidents, in accordance with embodiments of the presentinvention.

DETAILED DESCRIPTION

FIG. 1 illustrates a vehicular accident detection system 100, inaccordance with embodiments of the present invention. Sensors andcameras within automobiles may retrieve statistics such as speed, engineconditions, brake status, airbag deployment etc. The statistics are usedto detect mechanical faults associated with an accident. System 100analyzes the statistics applied via a set of traffic rules andco-relates the resulting information with a relative positioning withrespect to additional entities involved in an accident to accuratelypin-point a cause of the accident. A determined cause may benefitdrivers, insurance agencies, law enforcement agencies, emergencyresponders, etc. and improve overall road safety. System 100 enables amethod for predicting a cause of a vehicular accident. System 100 maydetect a most likely cause and any respective liable parties withrespect to a vehicular accident by:

-   1. Gathering: vehicular accident information including a    location/site of the accident, a relative positioning of vehicles or    objects involved in the accident, and traffic rules applicable to    the accident site.-   2. Applying business logic (e.g., language syntax, business rules,    etc.) and weights to the aforementioned data in combination with    relevant historical data to anticipate or predict an event based on    historical patterns or algorithmic outcomes thereby accurately    determining a cause and sequence of events that may have led to an    accident.

Alternatively, system 100 enables a method for building predictivemodels with respect to vehicular accidents based on the aforementioneddata and relevant historical data.

Additionally, system 100 enables a method for building and executing aself-learning algorithm to adjust weights used to determine a mostlikely cause and any liable parties with respect to the vehicularaccident.

System 100 enables storage of automobile accident related informationsuch as, inter alia, a location, circumstances, causes, events withrespect to an accident, rules applicable at a location of the accident,etc. Additionally, system 100 leverages onboard sensors, camera devices,and accident detection mechanisms to gather related informationresulting in a determination of true causes and faults associated withan accident.

-   System 100 allows:-   1. Detection of rules governing vehicular accident circumstances.-   2. Generation of circumstance data for law enforcement officials,    insurance agencies, and involved parties (i.e., involved in an    accident) for proper fault detection, ticketing, claims, etc.-   3. Automatic transmission of information and alerts to emergency    response teams and hospitals-   4. Linking to satellites for uploading accident-related information,    images, passenger information to a repository for research and    associated processes.-   5. Improvement of driving mechanisms.-   6. Improvement of training and driving lessons for drivers.-   7. Improvement of road safety.

The above and other features of the present invention will become moredistinct by a detailed description of embodiments shown in combinationwith attached drawings. Identical reference numbers represent the sameor similar parts in the attached drawings of the invention.

Aspects of the present invention may take the form of an entirelyhardware embodiment, an entirely software embodiment (includingfirmware, resident software, microcode, etc.) or an embodiment combiningsoftware and hardware aspects that may all generally be referred toherein as a “circuit,” “module,” or “system.”

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

System 100 comprises a vehicle 101 comprising a computing system 101 acommunicably connected to onboard sensors and a camera(s) 122, a mapsand rules database(s) 114, satellites 123 a and 123 b, and a cloudrepository 125. Computing system 101 a comprises a location detectioncomponent 104, a traffic rules interpretation component 108, a relativepositioning component 110, and a diagnostic component 112. Computingsystem 101 a may be located internal to vehicle 100 as illustrated inFIG. 1. Alternatively, computing system 101 a (or portions of computingsystem 101 a) may be located external to vehicle 101. Additionally,computing system 101 a may be located internal to and external tovehicle 101.

Location detection component 104 is configured to communicate withsatellites 123 a (e.g., a global positioning satellite (GPS), analyzeassociated geographical maps, and pin-point an exact location of avehicular accident. Additionally, location detection component 104stores information associated with a last known location of a vehicle.Diagnostic component 112 is configured to communicate with satellites123 b (e.g., a global positioning satellite (GPS).

Traffic rules interpretation component 108 is configured to interprettraffic rules in reference to maps (i.e., retrieved from maps and rulesdatabase(s) 114) as a set of rules in a specified language. Thespecified language comprises a set of defined symbols representingdiffering traffic signs. The defined symbols are used in combinationwith logical operators (e.g., a logical “NOT”, “AND”, “OR”, etc. asdescribed in example 2, infra) to define traffic rules with a specifiedprecedence/weight applied to the rules applicable with respect to aspecific circumstance. The maps and traffic rules are stored on servers(e.g., maps and rules database(s) 114) accessible to traffic rulesinterpretation component 108 via application programming interfaces(APIs) and request/response mechanisms over wireless Internetconnections. The maps and rules may be periodically updated on theservers with current information. Additionally, traffic rulesinterpretation component 108 may locally cache the maps and the rulesfor offline use such that system 100 may operate in a geographical areacomprising scattered network coverage.

Relative positioning component 110 communicatively connected to onboardsensors (e.g., blind spot detectors, etc.) and a (360-degree) camera 122enables detection of: a relative positioning of entities involved in avehicular accident, a state of traffic signals at the time and site ofthe accident, and historical information associated with a vehicle'smovements/actions for a last specified time period (e.g., 30-60 seconds)of its drive path leading up to the accident. The 360-degree camerainformation provides geospatial insights as well as weather conditionsthat are ingested for effective cause analysis. Additional systemsensors may be auto-activated by information from other sensors such asa proximity of vehicles involved or near to an accident. Onboard sensorsand camera(s) 122 are configured to capture incidents within a specifiedtimeframe leading up to the accident and at a point of impact. Relativepositioning component 110 may additionally (and automatically) deployairbags and apply brakes to prevent accidents in addition toparticipating in accident detection in cases when an accident doesoccur. An output generated by relative positioning component 110 may begenerated when an accident is caused by human error. For example, adriver of a parked automobile does not signal a merge into a moving laneand initiates a driving process thereby failing to notice and yield toanother automobile and colliding with the other automobile.

Diagnostic component 112 comprises a set of rules to diagnosecircumstances and cause of an accident based on outputs from locationdetection component 104, traffic rules interpretation component 108, anda relative positioning component 110 at the time of an accident.

Location detection component 104, traffic rules interpretation component108, relative positioning component 110, and diagnostic component 112interact (at the time of an accident) as follows:

Upon activation of system 100, a location of a vehicle is known tolocation detection component 104 at all times. Therefore, a location ofthe vehicle (during involvement of an accident) is transmitted fromlocation detection component 104 to traffic rules interpretationcomponent 108 and relative positioning component 110. In response,traffic rules interpretation component 108 uses a specified language tointerpret traffic rules applicable to each point on a traffic map.Additionally, traffic rules interpretation component 108 analyzeslocation information (retrieved from location detection component 104)to derive traffic rules applicable to the accident location. Trafficrules interpretation component 108 transmits the traffic rules todiagnostic component 112. In response, relative positioning component110 determines a relative positioning of all entities involved in anaccident based on data associated with onboard touch and visual sensors,a state of the traffic signals at a time and site of the accident, andhistorical information associated with vehicle movement/actions a lastspecified timeframe of a drive path leading up to the accident. Thisinformation is passed to diagnostic component 112. In response,diagnostic component 112 uses information retrieved from traffic rulesinterpretation component 108 and relative positioning component 110 todetermine circumstances and a cause of the accident based on built-inlogic.

The following examples illustrate implementation scenarios executed byvehicular accident detection system 100 with respect to a currentaccident situation.

Example 1

Example 1 is associated with an accident occurring at an intersectionassociated with a driver operating an automobile A overrunning a redlight and impacted by an automobile B. In response, accident detectioncomponent 104 determines a location or site comprising coordinates (x,y, z) of the accident. Traffic rules component 108 outputs the followingtraffic rules applicable at the site of the accident:

-   1. Automobile A should have stopped as it was approaching a red    light.-   2. Automobile B was authorized to go as it was approaching a green    light.

The following example rules are represented in the traffic language witha set of symbols and logical operators. For example, a symbol crepresents an automobile, a symbol RL represents a red light, asymbol→represents the term approaching, a symbol GO represents that anautomobile may proceed, and a symbol STOP represents that an automobileshould stop. Therefore, a simple rule indicating that an automobile cshould STOP when approaching a red light (RL) may be represented by theequation: c→RL=STOP. Likewise, a simple rule that an automobile c may GOwhen approaching a green light (GL) may be represented by the equation:c→GL=GO. In response, relative position component 110 is enabled todetermine a relative positioning of automobiles A and B involved in theaccident with respect to a location of the accident determined by theaccident detection component 104 (i.e., accident detection component 104determines a location/site comprising coordinates (x, y, z) of theaccident). Additionally, relative position component 110 determines thatthe traffic light was red for automobile A but green for automobile Band that automobile A approached the accident site through the redtraffic light as follows:

A→RL=GO (automobile A proceeded while approaching a Red Light)

B→GL=GO (automobile B proceeded while approaching a Green Light)

Diagnostic component 112 uses the information derived from traffic rulescomponent 108 and relative position component 110 and comparesdetermined accident circumstances and related information outputted fromrelative position component 110 with the traffic rules retrieved frommaps and rules database(s) 114 and outputted from traffic rulescomponent 108 as follows:

A→RL=GO violates c→RL=STOP traffic rule output from traffic rulescomponent 108.

B→GL=GO is in compliance with c→GL=GO traffic rule output from trafficrules component 108.

Therefore, diagnostic component 112 determines that automobile A'sdriver was at fault.

Example 2

Example 2 describes an accident caused by human error with respect totraffic signals that are not explicitly involved. In this example, anaccident occurs when a driver of a parked automobile X does not providean adequate signal (in advance) to merge into a lane comprising movingtraffic. The driver of parked automobile X does not detect and yield toan additional automobile Y proceeding forward in the same lane. Theautomobile Y is determined to be traveling below a posted speed limitbut is unable to stop and prevent a collision when automobile X's driversuddenly initiates motion from a parked position without propersignaling or time to react. The accident appears to indicate thatautomobile Y has collided with automobile X from a side or rear sectionbut the actual fault is with the driver of automobile X who did notsufficiently signal intentions in advance. Additionally, the driver ofautomobile X did not detect automobile Y suddenly initiating motion in asame lane from a parked position on the side of the road. System 100automatically detects the correct scenario as follows:

Accident detection component 104 determines (e.g., from satellite basedcoordinates) a location or site comprising coordinates (x, y, z) of theaccident. Traffic rules component 108 outputs the following trafficrules applicable at the site of the accident:

-   1. Automobile X should have:    -   A. Enabled an indicator signal.    -   B. Detected an automobile in the lane to the left.    -   C. Initiated motion into the lane after enabling the signal and        detecting the automobile to the left.-   2. Automobile Y was authorized to move in the left lane below the    posted speed limit.

The following example rules are represented in the traffic language viathe following sub-rules indicated by the following equations:X→(LI AND NOCARONLEFT)=GOX→(NOTLI OR YESAUTOMOBILEONLEFT)=YIELD

-   -   Where:    -   Symbol LI represents that a left indicator signal is enabled.    -   Symbol NOAUTOMOBILEONLEFT represents that no automobile is        present on the left.    -   Symbol YESAUTOMOBILEONLEFT represents that there is an        automobile present on the left.    -   Symbol GO represents that an automobile may proceed.    -   Symbol YIELD represents that an automobile should yield.    -   Symbols NOT, AND, OR comprise logical operators.

Assuming that automobile X is attempting to merge into a lane to theleft of it's parked position, the rules state the following:

A first sub-rule states that: With a left indicator on (LI) and with noautomobile to the left (NOAUTOMOBILEONLEFT), automobile X may initiatemotion (i.e., merge into the moving lane).

A second sub-rule states that: With no left indicator on (NOTLI) or withan automobile to the left (YESAUTOMOBILEONLEFT), automobile X shouldYIELD (i.e., enable the indicator) and wait for the automobile Y to itsleft to pass before merging.

A second rule may be represented in the traffic language by thefollowing rule:

Y→(S<60)=GO

Where:

Symbol Y represent an automobile Y.

Symbol S_(Y) represents a speed of the automobile Y.

Therefore, a rule states at point (x, y, z), if a speed of automobile Yis determined to be less than 60 miles/hr, automobile Y may proceed.

In response, relative positioning component 110 generates the followingoutput:

-   1. A relative positioning and distance between automobiles X and Y    involved in the accident.-   2. Relative velocities of automobiles X and Y and if automobile Y    was moving below the posted speed limit.-   3. A time period indicating how long a left turn signal indicator    for automobile X was enabled before automobile X attempted to merge    into the lane.-   4. A distance between the automobiles X and Y during the time    period.

Diagnostic component 112 analyzes the information derived from trafficrules component 108 and relative position component 110 and comparesdetermined accident circumstances and related information outputted fromrelative position component 110 with the traffic rules outputted fromtraffic rules component 108 as follows:

Diagnostic component 112 determines that based on the following factors:the time period (i.e., indicating how long a left turn signal indicatorfor automobile X was enabled before automobile X attempted to merge intothe lane); a determined speed of automobile Y; and a determined distanceof travel of automobile Y before colliding with automobile X, the driverof automobile Y was unable to react swiftly enough to apply the brakesand prevent the collision. Therefore, it is determined that the driverof automobile X is at fault for the accident as the driver of automobileX:

-   1. Did not enable the left turn signal indicator in a timely manner.-   2. Did not detect automobile Y in the lane.-   3. Initiated motion suddenly (from a parked position by the side of    the road) thereby merging into the same lane as automobile Y.

The aforementioned rules and logic described, supra, may be extended tosupport complex country, state, and region-specific traffic rules aswell as detection of complex multi-automobile accident scenarios. System100 enables a method resulting in a determination with respect to causesfor accidents.

FIG. 2 illustrates an algorithm detailing a process flow executed bysystem 100 of FIG. 1 during a vehicular accident, in accordance withembodiments of the present invention. Each of the steps in the algorithmof FIG. 2 may be enabled and executed in any order by a computerprocessor executing computer code. The algorithm enables a method forautomatically detecting accidents and collisions via execution of logicand analytical models. In step 200, program code specifies that anaccident has occurred. The program code may determine that the accidenthas occurred by, inter alia, analyzing data from sensors (e.g., speedsensors, motion sensors, pressure sensors, impact sensors, etc.),determining airbag deployment, etc. In response, program code retrievesand aggregates (in step 204) accident related data from a database 202comprising data associated with past accidents, circumstances, andidentified causes and associated weighting factors. For example, theaccident related data may include, inter alia, determinedaccident/collision coordinates retrieved from a GPS device, etc. Thedatabase may include any type of physical or cloud based database.Database 202 may be periodically replicated into local systems. Table 1below illustrates examples of accident related data as follows:

TABLE 1 Circumstance Derived Most likely Circumstances DescriptionCause(s) Weight Cause Circumstance 1 Night time Inexpe- 1 Deer MovingAccident rienced Across Driver or Driving Path Driver Error Circumstance2 Deer related Drunk 3 Accident Driving Circumstance 3 Experienced Low 5Driver Visibility Accident Circumstance 4 Driver with Deer moving 7 goodDriving Across Record Driving Path Circumstance 5 Below Legal LimitAlcohol Related Circumstance N Utility Pole Related Accident

The accident related data may further include:

-   1. Historical information leading up to the accident such that each    entity involved in the accident/collision will collect the    historical information including systems installed around a site of    the accident/collision.-   2. A 360 degree sensor view around each entity involved in the    accident/collision. The sensor view data may be collected by    installed systems or entity related systems.-   3. Data retrieved from smart devices, cameras, telematics systems,    and onboard sensors (e.g., GPS receivers and accelerometers). The    retrieved data may include data associated with driver activities, a    speed of the vehicle/entity, and additional conditions within and    surrounding the entities involved in the accident/collision. The    retrieved data may be leveraged to:    -   A. Determine if the driver was using a smartphone hands free or        was texting.    -   B. Determine if there was detour or traffic congestion.-   4. Weather/environment conditions data that may be leveraged to:    -   A. Determine if precipitation is/was present.    -   B. Calculate a coefficient of friction and determine if loss of        traction was a factor.    -   C. If on a freeway overpass that is banked, determine a safe        velocity given precipitation.    -   D. Determine if there was a speed limit override given the        weather conditions.-   5. Data to determine entities/objects involved in the    accident/collision including vehicles/entities that were driven and    others that were part of the accident/collision. For example, a    utility pole, a fallen tree due to bad weather conditions, etc. The    aforementioned data may be leveraged to:    -   A. Examine a 360 view camera feed to determine if road debris or        falling objects from a vehicle in a front position were present        thereby causing a driver to suddenly change a lane.    -   B. Calculate a number of seconds to impact and for driver to        safely switch lanes.-   6. Data to determine relative positions of entities that may be held    responsible for the accident/collision (e.g., vehicles) along with    other objects involved in the accident/collision (e.g., trees,    fences, utility poles, deer, etc.).-   7. Data to determine a state and associated actions of the entities    involved in the accident/collision. The aforementioned data may be    leveraged to determine the following:    -   A. Evaluate if there was any attempt to avoid the accident.-   8. Data to determine a state of traffic signals or signs determining    the applicable rules at the accident/collision site.-   9. Data to determine additional information associated with the    entities and persons involved (in the case of auto accidents),    pictures of the drivers, if the driver was texting, whether air bags    were deployed, driver history and records, past history of the    entities involved, known problems with makes and models, etc.

In step 206, program code determines and outputs state and local trafficrules/applicable rules governing the site of the accident/collision inthe form of a specified language with syntax. The following usageexample illustrates the rules interpreted with the specified languagesyntax:

If the rules are available, then program code fetches the rules. Theavailable rules are interpreted in the language as follows: C1→RL=STOP.The aforementioned rule states that an entity C1 should stop withrespect to a red light. A coefficient of friction is calculated and itis determined if loss of traction was a factor in the accident.Additional rules may be stated with considerations with respect tomathematical formulae as follows: C2→(SC2<60)=GO. This rule states thatat coordinates (x, y, z), if a speed of vehicle C2 is less than 60miles/hr, the vehicle C2 may proceed. If the rules are not availablethen new rules are ingested. The rules are continuously ingested intothe system and modified in accordance with past and present accidentrelated data.

In step 208, program code applies programming logic to theaforementioned data. Additionally, program code applies: data, weightingfactors (as illustrated in tables 1 and 2) to accident causes, andadditional parameters resulting in an output specifying a likely causeof the accident/collision. The weighting factors may be associated withand applied based on various parameters such as, inter alia, excessivespeed, weather related issues such as snow, mechanical failure, etc. Thefollowing usage example illustrates the rules interpreted with thespecified language syntax:

An AND logical operator is enabled to determine a violation or causebased on an actual circumstance at the site of the accident/collisionand an associated traffic rule. For example, an actual circumstancebased on data collected from site was C1→RL=GO equaling true or 1.Therefore, it is determined that vehicle C1 initiated motion withrespect to a red light. The logical operator AND is applied to thetraffic rule C1→RL=STOP equaling true or 1 (i.e., (C1→RL=GO).(C1→RL=STOP), i.e. 1. 1, 1). Therefore, the system indicates that thevehicle C1 has violated a traffic rule and identifies that as a likelycause of the accident. Additionally, additional parameters such as acoefficient of friction are calculated and it is determined if a loss oftraction was a factor in the accident. The logic may additionallyconsider an identified slippery road, derive the coefficient offriction, and identify the aforementioned factors as likely causes. Aslippery road may be identified by antilock braking system and tractioncontrol system within the vehicle. Additionally, historical data andspecified circumstances associated with the accident may be analyzed. Amaximum coefficient of friction (μ) may be determined based onlongitudinal, side, and normal forces acting on the tires of theautomobile. Based on x, y, z factors derived from the data at the siteof the accident, each system will identify a most likely cause of theaccident based on logic and the weights with respect to each cause. Ahigher weight (based on the circumstances) is determined to comprise amore likely cause.

In step 210, program code transmits the aforementioned data toadditional accident detection systems. In step 212, program codecollects the aforementioned data from the additional accident detectionsystems. The additional accident detection systems may display differentcauses. Information associated with past accidents/collisions,accident/collision locations, terrain, weather conditions, etc. at theaccident location as well as determined causes from legal and otherperspectives may be ingested into the system. Additionally, additionalrelevant data associated with the accidents/collisions and likely causesand associated weighting factors defining a probability with respect toeach cause are ingested into the system. The weighting factors may beassociated with excessive speed, snow, mechanical failure, etc. and maybe considered in addition to further parameters such as a driver'sdriving history and records, prior driving issues, traffic tickets, etc.In step 214, program code leverages analytical models with respect tothe data to accurately determine a most probable (highest weight/score)cause of the accident/collision based on the data collected with respectto a specified accident/collision. For example, an accident involving anexperienced driver who has not had an incident within the past fiveyears and is driving a same vehicle during an accident may have causedan accident due to slippery conditions associated with snow even thoughhe/she was driving below the maximum speed limit. An additional examplemay specify that a driver accidentally spilled his coffee and burnthimself while texting and driving, lost focus on the road, and caused amulti automobile accident due to abrupt braking. A further example mayspecify that during an active deer season, an accident in which a deerand an experienced safe driver's vehicle were involved was a most likelycause due to the deer abruptly cutting across a path of the vehiclewithout adequate time for braking in an area with no deer warning sign.Therefore, based on the data stored in the database 202, likely accidentcauses are determined by each system in the vicinity of the accidentsite as illustrated in table 2 as follows:

TABLE 2 Derived Likely Most Likely Cause(s) Weight Cause(s)Inexperienced 1 Deer into Driver or Driver Oncoming Path Error DrunkDriving 3 Night Visibility 5 Deer in 7 Oncoming Path

In step 216, a most likely cause for the accident is displayed via anoutput display terminal. In step 218, program code uploadsaccident/collision circumstances, entities involved, and causes for theaccident to a Cloud repository 250 for research and additionalprediction leveraging predictive models. In step 220, program codetransmits notifications (associated with the uploaded data of step 218)to law enforcement agencies, emergency responders, insurance agenciesetc. In step 224, program code modifies differing associated parametersand rules to predict outcomes of accidents/collisions between two ormore entities. As additional accidents are analyzed and associatedcauses are detected, related data may be ingested into database 202thereby predicting accidents if it is determined that circumstancesCir1, Cir2 . . . , CirN are prevalent at a location of the accident.Therefore, based on cause detection and circumstances of past accidents,the weights associated with causes (e.g., in table 1, supra) aremodified accordingly. Circumstances with respect to every new accidentare matched along with program logic to predict a likely cause of anaccident. Determined likely causes may be further analyzed based on allcircumstances collected and a most likely cause may be determined basedon a highest weight (e.g., from table 1). Over time, data with respectto the circumstances evolve and associated weights are modified for moreaccurate cause predictions. The following example 3 illustrates theaforementioned self-learning feature for determining accident causes.

Example 3

Example 2 describes a scenario comprising automobiles C1 and C2 involvedin an accident. In response to the accident, a system residing withinautomobile C1 (e.g., a system such as system 101 a of FIG. 1) determinesthat based on derived circumstances Cir1, Cir2, Cir3 and Cir4, likelycauses for the accident comprise causes cs1, cs2, and cs3. The causescs1, cs2, and cs3 are analyzed with respect to applied weighting factors(e.g., derived from a weighting factor table) and a most likely cause isdetermined to comprise cause cs3. Additionally, in response to theaccident, a system residing within automobile C2 C1 (e.g., a system suchas system 101 a of FIG. 1) determines based on circumstances Cir1, Cir2,Cir4, and Cir5, likely causes for the accident comprise causes cs2, cs3,and cs4. The causes cs1, cs2, and cs4 are analyzed with respect toapplied weighting factors (e.g., derived from a weighting factor table)and a most likely cause is determined to comprise cause cs4. Based onthe aforementioned analysis from the systems of automobiles C1 and C2, anew cause prediction rule is derived based on circumstances Cir1, Cir2,Cir3, Cir4, and Cir5 and a most likely cause with a highest weightcomprises cause cs4 if the weight of cs4 is greater than cause cs3.Therefore, if another similar accident occurs and circumstances Cir1,Cir3, Cir4 and Cir5 are determined, it may be predicted (based onevaluating the new cause prediction rule) that cause cs4 comprises alikely cause. Additionally, a prediction in terms of weight with respectto other likely causes may be derived and a weight associated withcircumstance cs4 may be adjusted to reflect a cause with respect to aset or subset of circumstances. For example, if circumstances Cir1,Cir2, Cir3, and Cir4 comprise circumstances associated with an accidentand a cause cs3 comprises a higher weight than cause cs4, it may bedetermined that cause cs3 comprises most likely cause. However ifcircumstances Cir1, Cir2, Cir3, Cir4, and Cir5 comprise circumstancesassociated with an accident and a cause cs4 comprises a higher weightthan cs3, it may be determined that cause cs4 comprises a most likelycause. Therefore, the aforementioned process results in a more accurateaccident prediction based on a certain set of circumstances determinedovertime with respect to a learning process, additional data, and weightadjustments. Therefore, it may be determined that there is a chance(with varying degrees of probability) for an accident/collisionoccurring due to one of the likely causes that have contributed to theaccidents/collisions under those circumstances.

In step 226, program code learns and bolsters logic to determineaccident causes, adjust weights with respect to likely causes based onrules and conditions to increase accuracy. The data ingested from pastaccidents may enable an adjustment with respect to the weights appliedto the causes based on circumstances for accurate detection andprediction. In step 228, program code shares the data with accidentprevention systems thereby enabling a process for preventing acircumstance n from occurring if it is determined that (n−1)circumstances out of the n circumstances that have caused numerousaccidents/collisions in the past are existent at a location at aspecified point in time. Therefore, an accident/collision may beprevented.

FIG. 3 illustrates a computer apparatus 90 for retrieving, storing, andanalyzing vehicular accident related information to determine a causefor vehicular accidents, in accordance with embodiments of the presentinvention. The computer system 90 includes a processor 91, an inputdevice 92 coupled to the processor 91, an output device 93 coupled tothe processor 91, and memory devices 94 and 95 each coupled to theprocessor 91. The input device 92 may be, inter alia, a keyboard, amouse, a camera, a touchscreen, etc. The output device 93 may be, interalia, a printer, a plotter, a computer screen, a magnetic tape, aremovable hard disk, a floppy disk, etc. The memory devices 94 and 95may be, inter alia, a hard disk, a floppy disk, a magnetic tape, anoptical storage such as a compact disc (CD) or a digital video disc(DVD), a dynamic random access memory (DRAM), a read-only memory (ROM),etc. The memory device 95 includes a computer code 97. The computer code97 includes algorithms (e.g., the algorithm of FIG. 2) for retrieving,storing, and analyzing vehicular accident related information todetermine a cause for vehicular accidents. The processor 91 executes thecomputer code 97. The memory device 94 includes input data 96. The inputdata 96 includes input required by the computer code 97. The outputdevice 93 displays output from the computer code 97. Either or bothmemory devices 94 and 95 (or one or more additional memory devices notshown in FIG. 3) may include the algorithms of FIG. 2 and may be used asa computer usable medium (or a computer readable medium or a programstorage device) having a computer readable program code embodied thereinand/or having other data stored therein, wherein the computer readableprogram code includes the computer code 97. Generally, a computerprogram product (or, alternatively, an article of manufacture) of thecomputer system 90 may include the computer usable medium (or theprogram storage device).

In some embodiments, rather than being stored and accessed from a harddrive, optical disc or other writeable, rewriteable, or removablehardware memory device 95, stored computer program code 84 (e.g.,including the algorithm of FIG. 2) may be stored on a static,nonremovable, read-only storage medium such as a Read-Only Memory (ROM)device 85, or may be accessed by processor 103 directly from such astatic, nonremovable, read-only medium 85. Similarly, in someembodiments, stored computer program code 84 may be stored ascomputer-readable firmware 85, or may be accessed by processor 103directly from such firmware 85, rather than from a more dynamic orremovable hardware data-storage device 95, such as a hard drive oroptical disc.

Still yet, any of the components of the present invention could becreated, integrated, hosted, maintained, deployed, managed, serviced,etc. by a service supplier who offers to retrieve, store, and analyzevehicular accident related information to determine a cause forvehicular accidents. Thus the present invention discloses a process fordeploying, creating, integrating, hosting, maintaining, and/orintegrating computing infrastructure, including integratingcomputer-readable code into the computer system 90, wherein the code incombination with the computer system 90 is capable of performing amethod for retrieving, storing, and analyzing vehicular accident relatedinformation to determine a cause for vehicular accidents. In anotherembodiment, the invention provides a business method that performs theprocess steps of the invention on a subscription, advertising, and/orfee basis. That is, a service supplier, such as a Solution Integrator,could offer to retrieve, store, and analyze vehicular accident relatedinformation to determine a cause for vehicular accidents. In this case,the service supplier can create, maintain, support, etc. a computerinfrastructure that performs the process steps of the invention for oneor more customers. In return, the service supplier can receive paymentfrom the customer(s) under a subscription and/or fee agreement and/orthe service supplier can receive payment from the sale of advertisingcontent to one or more third parties.

While FIG. 3 shows the computer system 90 as a particular configurationof hardware and software, any configuration of hardware and software, aswould be known to a person of ordinary skill in the art, may be utilizedfor the purposes stated supra in conjunction with the particularcomputer system 90 of FIG. 3. For example, the memory devices 94 and 95may be portions of a single memory device rather than separate memorydevices.

While embodiments of the present invention have been described hereinfor purposes of illustration, many modifications and changes will becomeapparent to those skilled in the art. Accordingly, the appended claimsare intended to encompass all such modifications and changes as fallwithin the true spirit and scope of this invention.

What is claimed is:
 1. A vehicle accident detection and drivingmechanism improvement method comprising: automatically deploying, by acomputer processor of a computing system enabling a relative positioningcircuit of a vehicle, airbags of said vehicle in response to detecting avehicular accident involving said vehicle, wherein said relativepositioning circuit comprises programmable logic circuitry utilizingstate information of computer readable program instructions topersonalize electronic circuitry of said relative positioning circuit;automatically applying, by said computer processor enabling saidrelative positioning circuit of said vehicle, a braking mechanism ofsaid vehicle in response to detecting said vehicular accident involvingsaid vehicle; receiving, by said computer processor, locationcoordinates describing a location where said vehicular accidentoccurred; receiving, by said computer processor from a first pluralityof sensors, data associated with possible causes of said vehicularaccident, wherein said first plurality of sensors automatically detectand convert to said data: engine conditions of said vehicle, a brakestatus of said vehicle, and an airbag deployment of said vehicle;automatically activating, by said computer processor in combination withsaid first plurality of sensors, a second plurality of sensors capturingtime based incidents associated with a point of impact of said vehicleduring said vehicular accident; retrieving, by said computer processor,traffic related rules associated with a geographical location of saidlocation; analyzing, by said computer processor executing programminglogic, said data and said time based incidents with respect to saidtraffic related rules; determining, by said computer processor based onresults of said analyzing, parameters associated with mechanical issuesof said vehicle involved in said vehicular accident; determining, bysaid computer processor based on results of said analyzing, distractionparameters associated with distraction related events for a driver ofsaid vehicle involved in said vehicular accident; determining, by saidcomputer processor executing a diagnostic circuit diagnosing results ofsaid analyzing, said parameters, and said distraction parameters, apossible cause for said vehicular accident, wherein said diagnosticcircuit comprises programmable logic circuitry utilizing stateinformation of computer readable program instructions to personalizeelectronic circuitry of said diagnostic circuit; generating, by saidprocessor based on said possible cause for said vehicular accident, selflearning software code configured to automatically deploy mechanicalsystems of said vehicle for preventing an additional vehicular accidentand improving mechanisms of said vehicle; and automatically deploying,by said computer processor executing said self learning software code,said mechanical systems of said vehicle in response to detection of apossible vehicular accident involving said vehicle.
 2. The method ofclaim 1, further comprising: retrieving, by said computer processor,additional determined possible causes for said vehicular accident;additionally analyzing, by said computer processor, said possible causewith respect to said additional determined possible causes; applying, bysaid computer processor based on results of said additionally analyzing,weighting factors to said possible cause and said additional determinedpossible causes; determining, by said computer processor based on ahighest weighting factor of said weighting factors, a determined causefor said vehicular accident; and presenting, by said computer processorvia a display system, said determined cause for said vehicular accident.3. The method of claim 2, wherein said additional determined possiblecauses comprise causes selected from the group consisting of previousaccidents associated with said driver of said at least one vehicle,weather and terrain related conditions, and previous driver relatedrecords of said driver.
 4. The method of claim 2, further comprising:executing, by said computer processor, predictive modeling algorithmswith respect to said determined cause for said vehicular accident andadditional determined causes for additional vehicular accidentspreviously occurring at said location; generating, by said computerprocessor based on an application of results of said executing saidpredictive modeling algorithms, modified programming logic associatedwith said programming logic; and executing, by said computer processor,said modified programming logic with respect to related data associatedwith possible causes of an additional vehicular accident.
 5. The methodof claim 4, further comprising: predicting, by said computer processorbased on said results of said executing said programming logic, resultsof said executing said predictive modeling algorithms, and results ofsaid executing said modified programming logic, possible futurevehicular accidents at said location.
 6. The method of claim 4, furthercomprising: predicting, by said computer processor based on said resultsof said executing said programming logic, results of said executing saidpredictive modeling algorithms, and results of said executing saidmodified programming logic, possible future vehicular accidentscomprising similar circumstances with respect to said vehicularaccident.
 7. The method of claim 1, wherein said data associated withsaid vehicular accident comprises data selected from the groupconsisting of historical data associated with said location, sensor datafrom sensors located adjacent to said location, smart phone/GPS relateddata, environmental condition data, data retrieved from systems of saidat least one vehicle, traffic signal data, and data defining accidentcircumstances.
 8. The method of claim 1, wherein said parameterscomprise a determined coefficient of friction between tires of said atleast one vehicle and a driving surface.
 9. The method of claim 1,wherein said distraction parameters comprise distraction basedparameters selected from the group consisting of electronics baseddistraction parameters and food/drink based distraction parameters. 10.The method of claim 1, further comprising: providing at least onesupport service for at least one of creating, integrating, hosting,maintaining, and deploying computer-readable code in the computingsystem, said code being executed by the computer processor to implementsaid receiving said location coordinates, said receiving said data, saidretrieving, said analyzing, said determining said parameters, saiddetermining said possible cause, and said transmitting.
 11. A computingsystem comprising a computer processor coupled to a computer-readablememory unit, said memory unit comprising instructions that when executedby the computer processor implements a vehicle accident detection anddriving mechanism improvement method comprising: automaticallydeploying, by said computer processor enabling a relative positioningcircuit of a vehicle, airbags of said vehicle in response to detecting avehicular accident involving said vehicle, wherein said relativepositioning circuit comprises programmable logic circuitry utilizingstate information of computer readable program instructions topersonalize electronic circuitry of said relative positioning circuit;automatically applying, by said computer processor enabling saidrelative positioning circuit of said vehicle, a braking mechanism ofsaid vehicle in response to detecting said vehicular accident involvingsaid vehicle; receiving, by said computer processor, locationcoordinates describing a location where said vehicular accidentoccurred; receiving, by said computer processor from a first pluralityof sensors, data associated with possible causes of said vehicularaccident, wherein said first plurality of sensors automatically detectand convert to said data: engine conditions of said vehicle, a brakestatus of said vehicle, and an airbag deployment of said vehicle;automatically activating, by said computer processor in combination withsaid first plurality of sensors, a second plurality of sensors capturingtime based incidents associated with a point of impact of said vehicleduring said vehicular accident; retrieving, by said computer processor,traffic related rules associated with a geographical location of saidlocation; analyzing, by said computer processor executing programminglogic, said data and said time based incidents with respect to saidtraffic related rules; determining, by said computer processor based onresults of said analyzing, parameters associated with mechanical issuesof said vehicle involved in said vehicular accident; determining, bysaid computer processor based on results of said analyzing, distractionparameters associated with distraction related events for a driver ofsaid vehicle involved in said vehicular accident; determining, by saidcomputer processor executing a diagnostic circuit diagnosing results ofsaid analyzing, said parameters, and said distraction parameters, apossible cause for said vehicular accident, wherein said diagnosticcircuit comprises programmable logic circuitry utilizing stateinformation of computer readable program instructions to personalizeelectronic circuitry of said diagnostic circuit; generating, by saidprocessor based on said possible cause for said vehicular accident, selflearning software code configured to automatically deploy mechanicalsystems of said vehicle for preventing an additional vehicular accidentand improving mechanisms of said vehicle; and automatically deploying,by said computer processor executing said self learning software code,said mechanical systems of said vehicle in response to detection of apossible vehicular accident involving said vehicle.
 12. The computingsystem of claim 11, wherein said method further comprises: retrieving,by said computer processor, additional determined possible causes forsaid vehicular accident; additionally analyzing, by said computerprocessor, said possible cause with respect to said additionaldetermined possible causes; applying, by said computer processor basedon results of said additionally analyzing, weighting factors to saidpossible cause and said additional determined possible causes;determining, by said computer processor based on a highest weightingfactor of said weighting factors, a determined cause for said vehicularaccident; and presenting, by said computer processor via a displaysystem, said determined cause for said vehicular accident.
 13. Thecomputing system of claim 12, wherein said method further comprises:wherein said additional determined possible causes comprise causesselected from the group consisting of previous accidents associated withsaid driver of said at least one vehicle, weather and terrain relatedconditions, and previous driver related records of said driver.
 14. Thecomputing system of claim 12, wherein said method further comprises:executing, by said computer processor, predictive modeling algorithmswith respect to said determined cause for said vehicular accident andadditional determined causes for additional vehicular accidentspreviously occurring at said location; generating, by said computerprocessor based on an application of results of said executing saidpredictive modeling algorithms, modified programming logic associatedwith said programming logic; and executing, by said computer processor,said modified programming logic with respect to related data associatedwith possible causes of an additional vehicular accident.
 15. Thecomputing system of claim 14, wherein said method further comprises:predicting, by said computer processor based on said results of saidexecuting said programming logic, results of said executing saidpredictive modeling algorithms, and results of said executing saidmodified programming logic, possible future vehicular accidents at saidlocation.
 16. The computing system of claim 14, wherein said methodfurther comprises: predicting, by said computer processor based on saidresults of said executing said programming logic, results of saidexecuting said predictive modeling algorithms, and results of saidexecuting said modified programming logic, possible future vehicularaccidents comprising similar circumstances with respect to saidvehicular accident.
 17. The computing system of claim 11, wherein saiddata associated with said vehicular accident comprises data selectedfrom the group consisting of historical data associated with saidlocation, sensor data from sensors located adjacent to said location,smart phone/GPS related data, environmental condition data, dataretrieved from systems of said at least one vehicle, traffic signaldata, and data defining accident circumstances.
 18. The computing systemof claim 11, wherein said parameters comprise a determined coefficientof friction between tires of said at least one vehicle and a drivingsurface.
 19. The computing system of claim 11, wherein said distractionparameters comprise distraction based parameters selected from the groupconsisting of electronics based distraction parameters and food/drinkbased distraction parameters.
 20. A computer program product for vehicleaccident detection and driving mechanism improvement, the computerprogram product comprising: one or more computer-readable, tangiblestorage devices; program instructions, stored on at least one of the oneor more storage devices, to automatically deploy, via a relativepositioning circuit of a vehicle, airbags of said vehicle in response todetecting a vehicular accident involving said vehicle, wherein saidrelative positioning circuit comprises programmable logic circuitryutilizing state information of computer readable program instructions topersonalize electronic circuitry of said relative positioning circuit;program instructions, stored on at least one of the one or more storagedevices, to automatically apply, via said relative positioning circuitof said vehicle, a braking mechanism of said vehicle in response todetecting said vehicular accident involving said vehicle; programinstructions, stored on at least one of the one or more storage devices,to receive location coordinates describing a location where saidvehicular accident occurred; program instructions, stored on at leastone of the one or more storage devices, to receive from a firstplurality of sensors, data associated with possible causes of saidvehicular accident, wherein said first plurality of sensorsautomatically detect and convert to said data: engine conditions of saidvehicle, a brake status of said vehicle, and an airbag deployment ofsaid vehicle; program instructions, stored on at least one of the one ormore storage devices, to automatically activate, via said firstplurality of sensors, a second plurality of sensors capturing time basedincidents associated with a point of impact of said vehicle during saidvehicular accident, program instructions, stored on at least one of theone or more storage devices, to retrieve traffic related rulesassociated with a geographical location of said location; programinstructions, stored on at least one of the one or more storage devices,to analyze said data and said time based incidents with respect to saidtraffic related rules; program instructions, stored on at least one ofthe one or more storage devices, to determine parameters associated withmechanical issues of said vehicle involved in said vehicular accident;program instructions, stored on at least one of the one or more storagedevices, to determine distraction parameters associated with distractionrelated events for a driver of said vehicle involved in said vehicularaccident; program instructions, stored on at least one of the one ormore storage devices, to determine, via execution of a diagnosticcircuit diagnosing results of the analyses, the parameters, and thedistraction parameters, a possible cause for said vehicular accident,wherein said diagnostic circuit comprises programmable logic circuitryutilizing state information of computer readable program instructions topersonalize electronic circuitry of said diagnostic circuit; programinstructions, stored on at least one of the one or more storage devices,to generate, based on said possible cause for said vehicular accident,self learning software code configured to automatically deploymechanical systems of said vehicle for preventing an additionalvehicular accident and improving mechanisms of said vehicle; and programinstructions, stored on at least one of the one or more storage devices,to automatically deploy, via execution of said self learning softwarecode, said mechanical systems of said vehicle in response to detectionof a possible vehicular accident involving said vehicle.